Classification assigns an entity to a category on the basis of feature
values encoded from a stimulus. Provided they are presented with
sufficient training data, inductive classifier builders such as C4.5 are
limited by encoding deficiencies and noise in the data, rather than by
the method of deciding the category. However, such classification
techniques do not perform well on the small, dirty /or and dynamic data
sets which are all that are available in many decision making domains.
Moreover, their computational overhead may not be justified. This paper
draws on conjectures about human categorization processes to design a
frugal algorithm for use with such data. On presentation of an
observation, case-specific rules are derived from a small subset of the
stored examples, where the subset is selected on the basis of similarity
to the encoded stimulus. Attention is focused on those features that
appear to be most useful for distinguishing categories of observations
similar to the current one. A measure of logical semantic information
value is used to discriminate between categories that remain plausible
after this. The new classifier is demonstrated against neural net and
decision tree classifiers on some standard UCI data sets and shown to
perform well.

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